The advent of a global communications network such as the Internet has perpetuated the exchange of enormous amounts of information. Additionally, the costs to store and maintain such information have declined, resulting in massive data storage structures, which can be accessed at a later time.
For example, history data can now be employed for analysis that supports business decisions at many levels, from strategic planning to performance evaluation of a discrete organizational unit. Such can further involve taking the data stored in a relational database and processing the data to make it a more effective tool for query and analysis. Accordingly, data warehousing and online analytical processing (OLAP) represent vital tools that support business decisions and data analysis. In general, a data warehouse is a nonvolatile repository for an enormous volume of organizational or enterprise information (e.g., 100 MB-TB). These data warehouses are populated at regular intervals with data from one or more heterogeneous data sources, for example from multiple transactional systems. Moreover, this aggregation of data provides a consolidated view of an organization from which valuable information can be derived. Even though the sheer volume can be overwhelming, the organization of data can help ensure timely retrieval of required information.
Data in data warehouses are often stored in accordance with a multidimensional database model. Such data can conceptually be represented as cubes with a plurality of dimensions and measures, rather than relational tables with rows and columns. A cube includes groups of data such as three or more “dimensions” and one or more “measures”. Dimensions are a cube attribute that contain data of a similar type. Each dimension has a hierarchy of levels or categories of aggregated data. Accordingly, data can be viewed at different levels of detail. Measures represent real values that require analysis. The multidimensional model can further optimized to deal with large amounts of data. In particular, it allows users to execute complex queries on a data cube. For example, online analytical processing (OLAP) is almost synonymous with multidimensional databases.
OLAP is a key element in a data warehouse system, and describes category of technologies or tools utilized to retrieve data from a data warehouse. Such tools can extract and present multidimensional data from different points of view to assist and support managers and other individuals examining and analyzing data. The multidimensional data model is advantageous with respect to OLAP as it enables users to easily formulate complex queries, and readily filter or slice data into meaningful subsets, among other things. There exists two basic types of OLAP architectures MOLAP and ROLAP. MOLAP (Multidimensional OLAP) utilizes a true multidimensional database to store data. ROLAP (Relational OLAP) utilizes a relational database to store data and is mapped so that an OLAP tool sees the data as multidimensional. Thus, multidimensional databases are often generated from relational databases.
Such analysis tools help reduce access times for extreme amounts of data. For example, by employing these tools, a user can ask general questions or “queries” about the data rather than retrieve all the data verbatim. Thus, “data about data” or metadata helps expedite the query process and reduce the required network bandwidth. Moreover, there exists an increasing demand for a more customized information delivery, in light of the exponentially expanding sizes of data stores. In general, conventional analysis servers cannot be tailored according to a user's unique requirements.